Assets
Assets is the file manager that feeds the RAG Knowledge Base in Turing ES. Content managers and administrators use it to upload documents (PDFs, Word files, spreadsheets, and more) that are automatically indexed as vector embeddings and made available to AI Agents for semantic search. It is the bridge between your files and the Knowledge Base — everything uploaded here becomes part of what the AI can reference when answering questions.
The Assets section (/console/asset) is a file manager with built-in RAG training capabilities. It is available in the Management section of the sidebar and is only visible when MinIO is enabled.
Assets serves as the Knowledge Base for AI Agents — every file uploaded here can be indexed as vector embeddings and queried by the LLM via tool calling. For the conceptual overview of how this fits into the GenAI architecture, see Generative AI & LLM Configuration.
Assets and all RAG Knowledge Base features require MinIO to be configured. See MinIO Configuration at the bottom of this page.
Layout
The interface uses a resizable dual-panel layout:
- Left panel — file and folder listing with the action toolbar
- Right panel — inline preview of the selected file
A breadcrumb at the top of the left panel shows the current folder path and allows navigation to any parent level. A Root button returns to the top-level folder instantly.
File Table
The file listing displays the following columns:
| Column | Description |
|---|---|
| Name | File or folder name |
| Size | File size in human-readable format |
| Type | MIME type or folder indicator |
| Last Modified | Date and time of the last modification |
| AI | Training status — a checkmark indicates the file has been indexed as embeddings, with a tooltip showing the training timestamp |
| Actions | Per-row download and delete buttons |
File Management
Upload Files
Files are uploaded to the current folder via a file picker dialog (click Upload Files). Multiple files can be selected in one operation. Uploads are sent to:
POST /api/asset
After upload, an asynchronous event automatically triggers individual AI indexing for each uploaded file — no manual training step is needed for new uploads.
Create Folder
A dialog prompts for a folder name. Folders can be nested to any depth and are navigated via the breadcrumb.
Download
Each file has a dedicated download button that preserves the original filename.
Delete
Files and folders can be deleted via an inline button. A toast notification confirms completion. When a file is deleted, its embeddings are automatically removed from the vector store.
Renaming files or folders is not currently supported. To rename a file, download it, delete the original, and re-upload with the new name.
Supported File Formats for Training
Apache Tika is the text-extraction engine used during AI Training. It supports a broad range of document types:
| Category | Formats |
|---|---|
| Documents | PDF, DOCX, DOC, ODT, RTF, EPUB |
| Spreadsheets | XLSX, XLS, ODS, CSV |
| Presentations | PPTX, PPT, ODP |
| Web / Markup | HTML, XHTML, XML |
| Plain text | TXT, LOG, Markdown |
| EML, MSG, MBOX | |
| Images (with OCR) | PNG, JPEG, TIFF, BMP, GIF (requires Tesseract) |
Files that Tika cannot extract text from (e.g. binary executables, ZIP archives without textual content) are silently skipped during training with no error counted.
Preview Panel
Selecting a file opens an inline preview in the right panel without leaving the page. Supported formats:
| Category | Formats |
|---|---|
| Images | PNG, JPEG, GIF, WebP, SVG, BMP |
| PDFs | Rendered via iframe |
| Video | MP4, WebM, OGG (with player controls) |
| Audio | MP3, OGG, WAV, WebM (with player controls) |
| Text | TXT, CSV, HTML, CSS, JS, JSON, XML |
Panel actions:
- Maximise — opens fullscreen view (press
Escto close) - Download — downloads the file directly from the preview panel
- Close — collapses the preview panel
The panel footer displays the file size, content type, modification date, and file extension.
AI Training (RAG)
The AI training features are only available when ragEnabled=true and an embedding model and embedding store are configured in Administration → Global Settings → RAG Settings.
Training Status per File
The AI column in the file table shows the indexing state of each file:
- ✅ Checkmark — file has been indexed; hover to see the training timestamp
- (empty) — file has not yet been indexed
Automatic Training on Upload
When a file is uploaded, Turing ES dispatches an asynchronous event that indexes the file individually without any user action required. Similarly, when a file is deleted, its embeddings are automatically purged from the vector store.
Batch Training
To index all existing files at once — useful after enabling RAG on an existing installation, or after changing the embedding model — use the "Train AI with Assets" button.
Batch training steps for each file:
- Download file bytes from MinIO
- Extract plain text via Apache Tika — supports PDF, DOCX, XLSX, PPTX, HTML, TXT, and images (with OCR)
- Truncate text to 100,000 characters
- Split into chunks of 1,024 characters
- Generate embeddings and store in the configured vector store
- Write a record to
asset_training_recordwith timestamp
Progress monitoring — while the batch is running, the UI polls every 3 seconds and displays:
X / Y files processed, Z errors
Training states: IDLE → RUNNING → COMPLETED / FAILED
If you change the Default Embedding Model in Administration → Global Settings → RAG Settings, all existing embeddings become invalid. Run "Train AI with Assets" again to re-index all files with the new model.
Embedding Metadata
Each chunk stored in the vector store carries the following metadata:
| Field | Value |
|---|---|
source | "minio-asset" |
objectName | Full object path in MinIO |
objectPath | Folder path within the bucket |
fileName | Original filename |
contentType | MIME type of the source file |
size | File size in bytes |
This metadata is used by AI Agents when returning search results, so the LLM can cite the source file and provide context about where the information came from.
Text Extraction Limits
There is no hard file size limit for uploads — MinIO accepts files of any size. However, during AI Training the extracted text is truncated to 100,000 characters before chunking. For very large documents this means only the first portion of the content is indexed. The truncation limit is defined by TurRagUtils.MAX_TEXT_LENGTH.
How AI Agents Use the Knowledge Base
Once files are indexed, AI Agents can query the knowledge base via four built-in tools:
| Tool | Description |
|---|---|
search_knowledge_base | Semantic similarity search across all indexed chunks |
knowledge_base_stats | Returns total files, chunks, and storage size |
list_knowledge_base_files | Lists all indexed files, with optional keyword filter |
get_file_from_knowledge_base | Retrieves the full indexed content of a specific file |
For details on configuring AI Agents and tools, see AI Agents and Tool Calling.
MinIO Configuration
MinIO must be enabled and configured before Assets becomes available:
turing.minio.enabled=true
turing.minio.endpoint=http://minio:9000
turing.minio.accessKey=minioadmin
turing.minio.secretKey=minioadmin
turing.minio.bucket=turing-assets
The bucket (turing-assets by default) is created automatically on startup if it does not exist.
With Docker Compose, add the MinIO service alongside Turing ES:
minio:
image: minio/minio
ports:
- "9000:9000"
- "9001:9001"
environment:
MINIO_ROOT_USER: minioadmin
MINIO_ROOT_PASSWORD: minioadmin
command: server /data --console-address ":9001"
volumes:
- minio_data:/data
The MinIO web console is available at http://localhost:9001 when running locally via Docker Compose. Use it to inspect buckets and verify that files are being stored correctly.
REST API Reference
All endpoints are under /api/asset and require authentication.
| Method | Endpoint | Description |
|---|---|---|
GET | /api/asset?prefix= | List files and folders in a prefix (folder) |
POST | /api/asset | Upload one or more files (multipart/form-data) |
GET | /api/asset/download?objectName= | Download a file (Content-Disposition: attachment) |
GET | /api/asset/preview?objectName= | Preview a file inline (Content-Disposition: inline) |
GET | /api/asset/metadata?objectName= | Retrieve file metadata (size, type, date) |
POST | /api/asset/folder?path= | Create a new folder |
DELETE | /api/asset?objectName= | Delete a file or folder |
POST | /api/asset/train | Start batch AI Training |
GET | /api/asset/train/status | Poll current training status |
GET | /api/asset/train/records?objectNames= | Get training timestamps for specific files |
Related Pages
| Page | Description |
|---|---|
| Embedding Stores & Models | Configure vector database backends and embedding models used by AI Training |
| Tool Calling | All 27 native tools, including the four Knowledge Base tools |
| AI Agents | Compose agents that query the Knowledge Base via tool calling |
| Generative AI & LLM Configuration | Platform-wide GenAI settings and RAG architecture overview |